Method for supplying a program-aided information system with specific positional information

ABSTRACT

Disclosed is a method for supplying a program-aided information system with specific location information, in which the information system provides at least one selection of certain location-dependent information on the basis of a person-specific or object-specific location which is detectable by a sensor. The present invention is distinguished by the combination of the following steps: detection of positional data for a person-specific or object-specific location by a sensor, transformation of said sensor-detected positional data into a location representing form, which is associated with a reference system, within which said positional data can be spatially attributed, as well as being associated with a hierarchical structure, combination of said location representing forms in a location set and/or in form of positional vectors in which said positional data of at least two locations are linked in a prescribed order, and/or formation of location relations and/or positional vector relations between the locations, persons or objects within so-called positioned location sets, and application of operations for determining the matching of locations as a basis of generating or providing location-dependent person-specific or object-specific information.

TECHNICAL BACKGROUND

The present invention relates to a method for supplying a program-aidedinformation system with specific positional information, in which theinformation system provides at least one selection of certain positionalinformation on the basis of a person-specific or object-specificposition which is detectable by a sensor.

Such type methods are based on program models for handling positionalinformation in computer programs, which provide their users informationbased on where they are currently located or where they will be locatedin the future. In these computer programs, users receive exactly thatinformation they actually require at the time and at the location wherethe respective need arises.

The dimension “location” therefore takes on an essential aspect by meansof which supplying users with information is optimized in such typecomputer programs. This aspect plays a significant role in various ways.For instance, users' need for certain information is, for example,dependent on where the user him/herself is located. Certain informationis only needed at certain locations. Furthermore, the information itselfwhich can be potentially provided to a user may in some cases be relatedto a location, i.e. it is relevant only for certain locations or itpossesses at a certain location greater information content for theusers. Even communication media, which employ such computer programs toprovide a user with the desired information are dependent on where theuser is located.

Therefore, such type computer programs must be able to processpositional information in connection with users' information needs, theinformation itself, the communication media and finally with the currentand future locations of the users and of other relevant objects. Forthis purpose, sensor systems are needed that are able to locate personsand objects. The information supplied by these sensors must also berepresentable and processable.

PRIOR ART

Presently, there are numerous computer programs available that provideinformation to users based on their current or future location. Suchtype programs are called Location Based Services and have all in commonthat they contain a data model for possible locations of persons andobjects.

In principle, there are two possible ways to represent locations in adata model. They can be imaged in the form of geometric data, i.e.related to an n-dimensional coordinate system, or as symbolic data, i.e.as a set of symbols or names, which are linked via relationships.Although today most prior art systems are confined to one of thepossible location representations, first attempts to integrategeographic and symbolic positions have been undertaken. However, thelocation models presently employed have a number of limitations, whichmake them unsuitable for supplying person-specific, needs-orientedinformation.

For one thing, these models and the systems in which they are utilizedare based on users' static information needs which the systemestablishes itself. Users cannot or only to a limited extent influencethese needs. Furthermore, at this time, a computer program usuallyutilizes only one single sensor system for locating. For this reason,each program only covers a narrow partial region of possible positionalinformation.

The models used all employ a different semantic. Presently there is noknown uniform representation of location in computer programs. Moreover,in particular, it is only possible to a limited extent to transform onelocation for which there is a certain form of representation into alocation with another form of representation. This is especially thecase with different symbolic locations. Such a transformation, however,is essential in order to adequately process positional information inthe various represented areas in which it is relevant for supplyingneeds-oriented information.

Prior art programs have not yet or only unsatisfactorily solvedproviding information regarding the relationships of locations to eachother important for the representation of locations, such as distance,inclusion relationships, i.e. checking whether a location is containedin another location, for example room 23 is included on the 2^(nd) floorof house X, and overlapping. Prior art programs also cannot or only to asmall extent image relationships between locations and persons,respectively between locations and objects, i.e. individual informationcannot be retrieved or supplied based on a person's or an object'scurrent location.

SUMMARY OF THE INVENTION

Based on the aforedescribed state of the art, the object of the presentinvention is to provide a method of supplying a program-aidedinformation system with specific positional information, in which theinformation system provides at least one selection of certain positionalinformation on the basis of a sensor-detectable, person-specific orobject-specific position, in such a manner that the method can beemployed independent of the type or dimension of the sensor signals usedfor locating the respective person or the respective object. Inparticular, a computer-aided database structure for positions should beprovided which permits simple and random adaptation to prior-artlocating systems. Moreover, the intention is to improve the precisionwith which the determination of the location of a respective person or arespective object is carried out on the basis of the positionalinformation acquired by a locating system. Finally, the aim is toprovide selectively and specifically a located person, respectively acorresponding located object, with position-specific information.

The solution to the object of the present invention is set forth inclaim 1. Advantageous further developing features of the inventive ideaare given in the subclaims and, in particular, in the followingdescription.

A key element of the present invention is that a method of supplying aprogram-aided information system with specific positional information,in which the information system provides a selection of certainpositional information on the basis of a sensor-detectable,person-specific or object-specific location, comprises the followingprocess steps:

In a first step, a technical locating system detects by means of sensorsthe position at which, for example, a person currently is located. Thepositional data acquired by sensors in this manner are then transformedinto a location representing form, the positional data being associatedwith a reference system, within which the positional data can beattributed spatially, as well as being associated with a hierarchicalstructure.

The location representing forms, each associated with a correspondingreference system and with the hierarchy particular to the respectivereference system, are then combined in a location set and/or in the formof positional vectors, in which the location representing forms of atleast two locations are linked in a defined order. Alternatively to thepreceding step of forming location sets, respectively of formingpositional vectors, or also combinations thereof, subsequently locationrelations and/or positional vector relations between locations andpersons, respectively between locations and objects, are formed withinso-called positioned location sets in order to finally permit generatingor providing location-dependent person-specific or object-specificinformation by carrying out operations if locations match, i.e. ifpositional data obtained by the position sensors and the locationsstored in information requests match.

In the invented method, the positional data acquired by sensors aretransformed into location representing forms, for example, in the formof the coordinate values of a reference system, by means of so-calledsensor adaptors, which represent special parts of a computer program.The positional data transformed into such a type location representingform are grouped into location sets or positional vectors, which may beconsidered as the basic forms of representation of locations. Locationsets are collections of unsorted location information which can eithercomprise one or a multiplicity of elements. Location sets containingexactly one element image so-called atomic locations, whereas locationsets containing more than one element contain combined locations orlists of locations. The single locations, respectively positionalinformation, in such location sets are linked via Boole's operators.Positional vectors contain locations in a fixed order on their nodes,permitting in this manner imaging routes. The edges in positionalvectors provide information about the distance between the locationnodes that they link. They can also be a location set or a positionalvector.

A tree structure is provided for the order of the locations in relationto each other. The tree structure permits ordering locationshierarchically and thus imaging complex location structures andso-called inclusion relationships, i.e. it is possible to check whether,for example, a room x on floor y is located in a building z.

Contrary to the state of the art described in the introduction,locations themselves are not subdivided into different classes,respectively into different reference systems, such as, for example, asolely geographic (longitude, latitude) or solely symbolic (locationname, street name, etc.) reference system. But rather, using sensoradaptors, the location model, respectively the method, associates everylocation with a reference system to which this location belongs. Thesereference systems contain the characteristics of the locations belongingto the system including their dimensions, admissible value ranges, therelationships of the dimensions to each other and to the dimensions ofother reference systems.

Furthermore, the method provides transformation rules which operate onthe reference systems and can transform locations from differentreference systems into each other, thereby permitting checking locationsfor inclusion, parity or intermediate spaces both for locations based onthe same reference system by this reference system and for locationswith different reference systems based on the transformation rules.

Furthermore, the location model, respectively the method, defines therelationship of persons and objects to locations by modeling so-calledprepositions. Prepositions can be attributed to locations of a locationset or of a positional vector. Moreover, distance information can beadded to the prepositions. Distances usually consist of one measuringunit, which may be a metric, temporal or positional unit, a quantityunit or an operator. Distances are also employed at other points in thelocation model, in particular, in the reference systems. Thus, it ispossible to determine distances between locations and persons,respectively location and objects, and between single locations.

Moreover, the method is able to image the precision and the probabilityof positional data, which is, particularly relevant for integratingdifferent position sensors, which often deliver positional data out offocus with respect to graininess and matching of the actual with thefound location. Moreover, reference systems, admissible prepositions,distance data and value ranges can also be extended dynamically if auser program so requires.

Furthermore, the method makes it possible to manage informationuniformly on locations with regard to position sensors,location-specific and/or person-specific information requests,communication channels and information even in computer programs. Inthis manner, computer programs are enabled to extend the dominatingtrend to personalization and individualization of the provided servicesand information even to the dimension location. Thus computer programusers only receive the information they actually need and is relevant tothem at the place where they are located.

The functionality provided by the method represents a considerable addedvalue for users compared to present computer programs and offers theirvendors considerable competitive advantages. These competitiveadvantages are augmented in that the present method and model can bedynamically extended and can be used in a great variety of fields ofapplication. Thus, the invented method can be easily integrated incomputer programs quickly and at little cost.

Moreover, computer program vendors can react quickly and cost-favorablyto changing program demands. The invented model can also be particularlyadvantageously utilized in innovative applications in the so-called“intelligent internet”. Here, the prevailing flood of information can bedammed by supplying information selectively; the information can also beprocessed and provided on the basis of location. As these types ofapplications are distinguished by a strong distribution of the dataprocessing stations, the present method's ability to generate and toextend make it especially suited to provide a uniform platform forintelligent internet applications.

The invented process has already been successfully implemented in atrial model in a platform for providing person-specific trafficinformation. In this platform, the registered users are informed on thebasis of the current traffic situation as they start out on a plannedtrip in order for them to arrive at a given destination at a given timetaking into account buffer times between receiving the information andthe time of departure as well as the user's preferred routes. Moreover,the user can also be provided with current information while drivingwith regard to the traffic situation on the route, possible trafficcongestion and alternative routes based on where the user happens to beat the time. In this example, there is a location-based informationrequest which says that a user would like to receive current congestioninformation for his route and his destination when he is driving on thehighway. This information request, therefore, contains positionalinformation in the form of “on the highway”. In order to satisfy thisrequest, the user is located by sensors after setting out on hisjourney. These sensor systems give the user's current location in theform of Gauss-Krüger geo-coordinates. The traffic information itself isprovided with positional data in the form of highway abbreviations inconnection with exit abbreviations and highway junctions. The locationmodel is responsible for the imaging, management and transformation ofthis positional data into their different forms. The positional datahighway, Gauss-Krüger coordinates and highway or exit/highwayabbreviations are imaged in positioned objects which each relate to asemantic reference system for transport lines, respectivelygeo-coordinates. The user's preferred routes are imaged as positionalvectors on the edges of which the means of transport is given. Whetherthe coordinates that supply a locating procedure match with the locationspecifications of the requested information is determined by means oftransformation algorithms. Furthermore, when this is the case, thesecoordinates are transformed into the location format in the trafficinformation.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is made more apparent in the following, withoutthe intention of limiting the scope or spirit of the inventive idea,using preferred embodiments with reference to the accompanying drawings.

FIG. 1 shows a schematic representation of the structure of the method

FIG. 2 shows the schematic representation of the reference systems.

WAYS TO CARRY OUT THE INVENTION, COMMERCIAL APPLICABILITY

FIG. 1 depicts a typical flow chart showing the structure and therelationships of the aforedescribed elements: location sets, positionalvectors, prepositions, etc.

First, the location set OM, containing the locations and/or positionalvectors, and the positional vectors OV, comprising at least twosensor-detected locations, are shown.

A structure S is associated with the locations O themselves. Thisstructure S images the so-called inclusion relationships between theindividual locations O. For this purpose, the structure S possessesnodes K and leaves B which form a tree thereby permitting a hierarchicalorder of the locations. For example, if the location is “room 1.29”,which corresponds to a leaf contained in the “building of company X”,which corresponds to a node contained for its part in the location“Dortmund”, which corresponds to the node.

In addition to these solely positional data, the present method enablesimaging prepositions P, i.e. relations between persons or object andlocations, such as for example “in”, “20 km before”, “outside of”. Inorder to permit this, the present method contains a positioned locationset PO, which contains so-called location relations OR and can,moreover, contain vector relations VR. Location relations and vectorrelations correspond to the previously described location sets OM andpositional vectors OV but extend them with the necessary propositions P.A location relation OR contains a location O and a preposition P whichrelates to this location O, for example “within a radius of 20 km ofMunich”. A vector relation VR contains analogously a positional vectorOV and a respective preposition P, for example “on the way to work”.

The class relation R ensures that the location relations OR and thevector relations VR are of the same type and permit passing downoperations OP to location relations and vector relations. Relation R isassociated with the described prepositions P. Prepositions P for theirpart may possess distance information D comprising quantity information,for example “within a radius of 20 km of Munich”, a unit of measure, forexample “km” and an operator, for example “within a radius of”.

FIG. 2 shows the order of the locations O to the reference systems RS.Each location O is described by a set of coordinates KO. Thesecoordinates KO unequivocally fix the position of the location O insidethe reference system RS. Coordinates KO do not only refer to physicalcoordinates, such as for example latitudes and longitudes supplied byGPS systems, but rather the coordinates of a location are any type ofvalues relating to a dimension, for instance the room number with thevalue 1.29 or the dimension “city name” with the value Munich. Thusthere are a number of alternative reference systems, in whichcoordinates define the position of a location, such as for examplegeographic RS, building RS, object RS or UTM-RS.

The method also takes into consideration the locating precision withwhich the different sensor systems operate to detect a location in thata specific precision G of the respective sensor system is associatedwith the values W of the sensor-detected coordinates.

In this way, it is possible to image that the single values W of thecoordinates, for example, are the coordinates of the dimensions D“longitude and latitude” and the precision of the positional data is 10m.

Thus, the coordinates KO relate exactly to one reference system RS givenby the sensor system. This reference system RS prescribes whichcharacteristics the respective coordinates KO must have. This occurs bypresetting the dimensions D to which the values W of the coordinates KOrelate and which at the same time define the valid value range.

Furthermore, the reference system RS determines which attributeslocations contain. As each reference system has a source, this sourceassigns a hierarchically higher position or a higher system limit foreach location. If it is helpful, the reference systems RS contain therelations between the locations of the reference system. For rooms, thiscan, for example, be a layout of the rooms imaged by the referencesystem showing the arrangement of the rooms.

Furthermore, the reference systems RS contain the transformation rulesfor transforming locations that relate to a reference system intolocations with a different reference system and therefore differentcoordinates.

Moreover, the reference systems are linked to sensor adaptors, which arespecial parts of a computer program that receives the locating data fromthe sensors (GPS receivers, transponder systems, electronic appointmentbooks, user entries, etc.) and transform them into the coordinate valuesof a reference system.

The invented method permits first and foremost to uniformly imagepossible locations in the computer program for supplyingperson-specific, needs-oriented information and thus to provide computerprogram users with relevant, location-dependent information.

The method, however, is of particular significance, if users'information requests depend on their current or predicted location. Suchis the case if an information request only occurs at certain locationsor if the information itself which is relevant to a user is defined bywhere the user is located.

In this case, the purpose of the invented method is to image current andfuture locations of users and objects. Furthermore, the method alsoimages positional data in connection with the users' informationrequest, for example “message, when Ms X enters the building” or “newsabout traffic congestion on my route”. An important task of computerprograms utilizing the invented method, is to check whether a current orpredicted location matches the location conditions of a user'sinformation request. For this purpose, data are obtained by sensors,which occurs by means of the above-mentioned sensor adaptors.

The sensors may be different type sensors. They can be roughlyclassified into genuine locating systems and derived locating systems.Genuine locating systems are sensors developed particularly fordetermining a location, such as for example GPS, transponder andinfrared systems. Derived locating systems are systems which originallyserved another purpose than locating, but which can be employed todetermine the location of persons and objects. Among them are, systemsfor determining working hours, electronic appointment books,room-occupation plans, explicit user entries, etc.

The sensor adaptors transform the determined data acquired by thelocating systems into locations according to the structure of thelocations in the location sets and positional vectors. Dependent on thetype of sensor and its use (installation position, purpose of thecomputer program), the adaptors determine which reference systems aresuited for imaging the employed sensor data. They transform the dataacquired in this manner into coordinate values of the respectivereference system. If the sensor data are directly available ascoordinates of a reference system (for example in GPS coordinates orsymbolic positions), direct imaging can occur on a location.

The thus imaged locations, if suited, are grouped into positionalvectors and into location sets. The structure of the locations, i.e.hierarchically higher or lower locations are imaged via the sources ofthe reference systems. The acquired sensor data are grouped with the aidof the sensor properties, such as precision, and the properties of thereference systems are transformed into distance informationcorresponding to the model and grouped into positioned location sets vialocation relations and vector relations.

An example: locating a person by ultrasonic means in a room at 3 mhorizontally from the left upper corner and 4 m vertically from the leftupper corner of the room. Locating precision is 10 cm. The objectreference system of the room is a chair at 3.5 m horizontally and 4 mvertically. From this, the location of the chair is derived with thedistance 50 cm.

None or only a few sensor adaptors are needed for the positional datacontained in the so-called information requests, i.e. the informationrequests are stored for each single user or object in a computer-aidedfile in which the respective information request for each location isstored, because the positional data are usually available in symbolicform or in rare cases as physical coordinates. Imaging the location setsand positional vectors, structures and prepositions occurs analogously.

If the positional data acquired by the sensors or established in theinformation requests are imaged according to the invented method,operations can occur on the positional data. These operations allow acomputer program to determine which information is relevant for the useron the basis of his/her location. For this, first of all the positionaldata in the information requests must be compared with the locationsdetected by the sensors. For this purpose, the model contains operationssuch as isIn( ), equal( ), howFarFrom( ), etc. These operations, whichare conducted on the locations, permit determining whether the locationsare the same, whether a location is contained in another one or how farlocations are apart.

Transformation rules are employed to carry out these operations if thelocations relate to different reference systems. First a suitedtransformation rule is found to transform the locations into a uniformreference system. Depending on the reference system, a uniformrepresentation in the form of physical coordinates or by transformationof the coordinates of one location into the coordinates belonging to thereference system of another location by means of stored imaging data,for example “building XY” corresponds to “Musterstr. 10, 12345Musterhausen, BRD” or imaging rules, for example algorithms fortransforming GPS data from a UTM system into GPS data from a WGS84system.

On the basis of this uniform form of representation, the parity of twolocations can be determined directly. Although two locations are not thesame but some parts may overlap, as the result of such a comparison, themethod provides probability data with which such overlappings areimaged. The distance between locations is converted on the basis ofphysical coordinates or via the properties of the respective referencesystem (for example position and dimensions of rooms in a building) intometric distances or intervals. Intervals relate to a specific travellingvelocity.

Furthermore, the described method permits comparing positional datadetected by sensors with the positional data of users' informationrequests either explicitly passed on by the user to the computer programor implicitly determined by the same. The result of such a comparisonallows the computer program to determine whether a user who is at acertain location needs information and if so what information isrelevant for the user taking into account his location.

1. A method for supplying a program-aided information system withspecific location information, in which the information system providesat least one selection of certain location-dependent information on thebasis of a person-specific or object-specific location which isdetectable by a sensor, wherein the combination of the following steps:detection of positional data for a person-specific or object-specificlocation by a sensor, transformation of said sensor-detected positionaldata into a location representing form, which is associated with areference system, within which said positional data can be spatiallyattributed, as well as being associated with a hierarchical structure,combination of said location representing forms in a location set and/orin form of positional vectors in which said positional data of at leasttwo locations are linked in a prescribed order, and/or formation oflocation relations and/or positional vector relations between thelocations, persons or objects within so-called positioned location sets,and application of operations for determining the matching of locationsas a basis of generating or providing location-dependent person-specificor object-specific information.
 2. The method of claim 1, wherein saidsensor detection of said positional data is conducted by means oftechnical locating systems.
 3. The method of claim 1, wherein saidtransformation of said sensor-detected positional data into a locationrepresenting form occurs using at least one sensor adaptor whichestablishes said reference system associated with the respectivepositional data.
 4. The method of claim 3, wherein said sensor-detectedpositional data are transformed into a location representing form in themanner of coordinate values within a reference system.
 5. The method ofclaim 1, wherein information or characteristics of the person locationsassociated with the respective location representing forms of thesensor-detected locations are stored in the respective reference system.6. The method of claim 1, wherein said locations are associated with ahierarchical structure in the form of a tree structure.
 7. The method ofclaim 1, wherein said sensor-detected positional data are combined in arandom order in said location set.
 8. The method of claim 1, whereinsaid positional vectors have at least two nodes at which asensor-detected location is provided in a fixed order, and a connectionis provided between two said nodes, along said connection informationregarding the route between two locations being linked, if need be, inthe form an additional location set and/or an additional positionalvector.
 9. The method of one claim 1, wherein said location representingforms are associated with information regarding the precision, withwhich the positional data is acquired by said technical locating system,and are associated with information regarding the distances within thereference system.
 10. The method of claim 9, wherein said positionaldata associated with information regarding the precision and thedistances within said location relations and/or said positional vectorrelations are grouped in said positioned location sets and areassociated with so-called prepositions, which describe a spatialrelative position between locations and persons, respectively betweensaid locations and objects, numerically and/or semantically.
 11. Themethod of claim 1, wherein the information requests are stored in theform of computer-aided data, and on the basis of said operations it isdetermined whether the positional data contained in said informationrequests match the positional data acquired by the position sensors. 12.The method of claim 11, wherein said operations check whether thelocation representing forms acquired from the sensor data and saidlocations in said information requests match or whether there is aninclusion relationship, and matching or numerical information regardingthe spatial distance of said location representing forms acquired fromthe sensor data and said respective location-dependent informationrequests is determined.